This skill helps users design precise and effective prompts for AI tools by applying the CLEAR framework, which emphasizes clarity, logic, examples, adaptation, and results. It enables users to build robust AI-driven solutions by clearly defining business requirements and expected outcomes.
claude skill add clear-framework-prompt-engineering-mkufhxwbThe CLEAR Framework Prompt Engineering skill is designed to assist users in crafting precise and effective prompts for AI tools by leveraging the CLEAR framework. This framework emphasizes five critical components: Clarity, Logic, Examples, Adaptation, and Results. By applying these principles, users can clearly define business requirements and expected outcomes, leading to the development of robust AI-driven solutions. This skill is particularly useful for those looking to enhance their AI automation capabilities and improve workflow automation processes. One of the key benefits of using the CLEAR Framework is the potential for significant time savings in the prompt design process. While specific time savings are not quantified, the skill allows users to streamline their prompt creation, enabling them to focus more on developing AI solutions rather than getting bogged down in the intricacies of prompt engineering. This efficiency is crucial for developers, product managers, and AI practitioners who need to deliver results quickly in a competitive environment. This skill is ideal for individuals involved in AI development, particularly those tasked with creating automated workflows or specific business solutions. Whether you are a developer looking to enhance your AI agent skills or a product manager aiming to implement effective AI automation strategies, the CLEAR Framework Prompt Engineering skill can help you achieve your goals. Practical use cases include crafting detailed AI prompts for business automation, designing prompts for creating automated workflows, and developing prompts for generating specific business solutions tailored to organizational needs. Implementation of the CLEAR Framework Prompt Engineering skill is categorized as intermediate, with an estimated time to implement of around 30 minutes. This makes it accessible for those with some prior experience in AI development. As organizations increasingly adopt AI-first workflows, mastering prompt engineering becomes essential. This skill not only enhances individual capabilities but also aligns with broader organizational goals of leveraging AI automation to drive efficiency and innovation.
1. Identify the problem or task you want the AI to solve or automate. 2. Use the CLEAR framework to craft your initial prompt, ensuring all components (clarity, logic, examples, adaptation, results) are included. 3. Input the crafted prompt into your AI tool, such as Claude or GPT. 4. Review the AI's output and refine the prompt iteratively to improve accuracy and relevance. 5. Validate the AI's final solution against your original business requirements.
Crafting detailed AI prompts for business automation
Designing prompts for creating automated workflows
Developing prompts for generating specific business solutions
No install command available. Check the GitHub repository for manual installation instructions.
Copy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Ensure your AI prompt includes the following components: 1. **Clarity**: Clearly define the problem with specific, measurable outcomes. For example, 'Create a one-page qualification SOP that identifies companies with 50+ employees in manufacturing that also expressed interest in automation within the last 90 days.' 2. **Logic**: Break down the process into sequential steps with clear decision points. Include logical reasoning that the AI can follow. 3. **Examples**: Provide scenarios or edge cases, detailing what should happen in each case. For instance, 'If a lead scores above 80 points, route to a senior sales rep; below 50, send to a nurture sequence.' 4. **Adaptation**: Be prepared to iteratively refine the prompt based on AI feedback. Engage in a conversation with the AI to improve the initial output. 5. **Results**: Define concrete metrics or criteria to validate the AI's outputs against business requirements, ensuring the solution meets expected results.
Create an outline for an automated lead qualification system. It is for a B2B manufacturing consultancy. Incoming leads will come from LinkedIn ads and cold email. The lead qualification system should score them based on company size. If there are 50+ employees, it’s 30 points. If the industry is manufacturing, add 25 points. If the engagement level, like a downloaded white paper, is 20 points or booked a demo, it's 40 points. Leads scoring 80+ should automatically route to a senior sales rep with a Slack notification. Leads scoring 50 to 79 should get scheduled for an automated demo booking. And leads below 50 should enter a six-week nurturing sequence with industry-specific case studies. The system should integrate with HubSpot and track conversion rates at each stage.
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